OpenRaiser/Intern-Atlas huggingface dataset:
Concept Saliency Maps to Visualize Relevant Features in Deep Generative Models
Venue: arXiv cs.LG 2019
Year: 2019

Evaluating, explaining, and visualizing high-level concepts in generative models, such as variational autoencoders (VAEs), is challenging in part due to a lack of known prediction classes that are required to generate saliency maps in supervised learning. While saliency maps may help identify relevant features (e.g., pixels) in the input for classification tasks of deep neural networks, similar frameworks are understudied in unsupervised learning. Therefore, we introduce a new method of obtaining saliency maps for latent representations of known or novel high-level concepts, often called concept vectors in generative models. Concept scores, analogous to class scores in classification tasks, are defined as dot products between concept vectors and encoded input data, which can be readily used to compute the gradients. The resulting concept saliency maps are shown to highlight input features deemed important for high-level concepts. Our method is applied to the VAE's latent space of CelebA dataset in which known attributes such as "smiles" and "hats" are used to elucidate relevant facial features. Furthermore, our application to spatial transcriptomic (ST) data of a mouse olfactory bulb demonstrates the potential of latent representations of morphological layers and molecular features in advancing our understanding of complex biological systems. By extending the popular method of saliency maps to generative models, the proposed concept saliency maps help improve interpretability of latent variable models in deep learning. Codes to reproduce and to implement concept saliency maps: https://github.com/lenbrocki/concept-saliency-maps
Accounting for Information Freshness in Scheduling of Content Caching
Venue: arXiv cs.DC 2019
Year: 2019

In this paper, we study the problem of optimal scheduling of content placement along time in a base station with limited cache capacity, taking into account jointly the offloading effect and freshness of information. We model offloading based on popularity in terms of the number of requests and information freshness based on the notion of age of information (AoI). The objective is to reduce the load of backhaul links as well as the AoI of contents in the cache via a joint cost function. For the resulting optimization problem, we prove its hardness via a reduction from the Partition problem. Next, via a mathematical reformulation, we derive a solution approach based on column generation and a tailored rounding mechanism. Finally, we provide performance evaluation results showing that our algorithm provides near-optimal solutions.
Deep Decentralized Reinforcement Learning for Cooperative Control
Venue: arXiv cs.MA 2019
Year: 2019

In order to collaborate efficiently with unknown partners in cooperative control settings, adaptation of the partners based on online experience is required. The rather general and widely applicable control setting, where each cooperation partner might strive for individual goals while the control laws and objectives of the partners are unknown, entails various challenges such as the non-stationarity of the environment, the multi-agent credit assignment problem, the alter-exploration problem and the coordination problem. We propose new, modular deep decentralized Multi-Agent Reinforcement Learning mechanisms to account for these challenges. Therefore, our method uses a time-dependent prioritization of samples, incorporates a model of the system dynamics and utilizes variable, accountability-driven learning rates and simulated, artificial experiences in order to guide the learning process. The effectiveness of our method is demonstrated by means of a simulated, nonlinear cooperative control task.
Privacy Enhanced Multimodal Neural Representations for Emotion Recognition
Venue: arXiv cs.LG 2019
Year: 2019

Many mobile applications and virtual conversational agents now aim to recognize and adapt to emotions. To enable this, data are transmitted from users' devices and stored on central servers. Yet, these data contain sensitive information that could be used by mobile applications without user's consent or, maliciously, by an eavesdropping adversary. In this work, we show how multimodal representations trained for a primary task, here emotion recognition, can unintentionally leak demographic information, which could override a selected opt-out option by the user. We analyze how this leakage differs in representations obtained from textual, acoustic, and multimodal data. We use an adversarial learning paradigm to unlearn the private information present in a representation and investigate the effect of varying the strength of the adversarial component on the primary task and on the privacy metric, defined here as the inability of an attacker to predict specific demographic information. We evaluate this paradigm on multiple datasets and show that we can improve the privacy metric while not significantly impacting the performance on the primary task. To the best of our knowledge, this is the first work to analyze how the privacy metric differs across modalities and how multiple privacy concerns can be tackled while still maintaining performance on emotion recognition.
Sign-Bit Shaping Using Polar Codes
Venue: arXiv cs.IT 2019
Year: 2019

A new polar coding scheme for higher order modulation is presented. The proposed scheme is based on multi-level coding (MLC) with natural labeling, where the bit-level corresponding to the sign-bit is generated in dependence on the previous bit-levels, such that the modulated symbols are distributed according to a target non-uniform distribution resulting in a shaping gain. This is realized by replacing the polar encoder in the sign-bit level by a successive cancellation (SC) decoder, such that the overall complexity is not increased compared to a conventional MLC scheme with polar codes. Numerical simulations show significant performance improvements of the proposed approach compared to conventional transmission schemes with uniform symbol distribution. By using more complex decoders (e.g. SC list decoder), the proposed scheme outperforms Gallager's random coding bound.
Real-time Bidding campaigns optimization using attribute selection
Venue: arXiv cs.LG 2019
Year: 2019

Real-Time Bidding is nowadays one of the most promising systems in the online advertising ecosystem. In the presented study, the performance of RTB campaigns is improved by optimising the parameters of the users' profiles and the publishers' websites. Most studies about optimising RTB campaigns are focused on the bidding strategy. In contrast, the objective of our research consists of optimising RTB campaigns by finding out configurations that maximise both the number of impressions and their average profitability. The experiments demonstrate that, when the number of required visits by advertisers is low, it is easy to find configurations with high average profitability, but as the required number of visits increases, the average profitability tends to go down. Additionally, configuration optimisation has been combined with other interesting strategies to increase, even more, the campaigns' profitability. Along with parameter configuration the study considers the following complementary strategies to increase profitability: i) selecting multiple configurations with a small number of visits instead of a unique configuration with a large number, ii) discarding visits according to the thresholds of cost and profitability, iii) analysing a reduced space of the dataset and extrapolating the solution, and iv) increasing the search space by including solutions below the required number of visits. The developed campaign optimisation methodology could be offered by RTB platforms to advertisers to make their campaigns more profitable.
Resolution-independent meshes of super pixels
Venue: arXiv cs.CV 2019
Year: 2019

The over-segmentation into superpixels is an important preprocessing step to smartly compress the input size and speed up higher level tasks. A superpixel was traditionally considered as a small cluster of square-based pixels that have similar color intensities and are closely located to each other. In this discrete model the boundaries of superpixels often have irregular zigzags consisting of horizontal or vertical edges from a given pixel grid. However digital images represent a continuous world, hence the following continuous model in the resolution-independent formulation can be more suitable for the reconstruction problem. Instead of uniting squares in a grid, a resolution-independent superpixel is defined as a polygon that has straight edges with any possible slope at subpixel resolution. The harder continuous version of the over-segmentation problem is to split an image into polygons and find a best (say, constant) color of each polygon so that the resulting colored mesh well approximates the given image. Such a mesh of polygons can be rendered at any higher resolution with all edges kept straight. We propose a fast conversion of any traditional superpixels into polygons and guarantees that their straight edges do not intersect. The meshes based on the superpixels SEEDS (Superpixels Extracted via Energy-Driven Sampling) and SLIC (Simple Linear Iterative Clustering) are compared with past meshes based on the Line Segment Detector. The experiments on the Berkeley Segmentation Database confirm that the new superpixels have more compact shapes than pixel-based superpixels.
Admiring the Great Mountain: A Celebration Special Issue in Honor of Stephen Grossbergs 80th Birthday
Venue: arXiv cs.NE 2019
Year: 2019

This editorial summarizes selected key contributions of Prof. Stephen Grossberg and describes the papers in this 80th birthday special issue in his honor. His productivity, creativity, and vision would each be enough to mark a scientist of the first caliber. In combination, they have resulted in contributions that have changed the entire discipline of neural networks. Grossberg has been tremendously influential in engineering, dynamical systems, and artificial intelligence as well. Indeed, he has been one of the most important mentors and role models in my career, and has done so with extraordinary generosity and encouragement. All authors in this special issue have taken great pleasure in hereby commemorating his extraordinary career and contributions.
Simultaneous Interference-Data Transmission for Secret Key Generation in Distributed IoT Sensor Networks
Venue: arXiv cs.IT 2019
Year: 2019

Internet of Things (IoT) networks for smart sensor nodes in the next generation of smart wireless sensing systems require a distributed security scheme to prevent the passive (eavesdropping) or active (jamming and interference) attacks from untrusted sensor nodes. This paper concerns advancing the security of the IoT system to address their vulnerability to being attacked or compromised by the advancement of future supercomputers. In this work, a novel embedded architecture has been designed and implemented for a distributed IoT network that utilizes a master-slave full-duplex communication to exchange the random and continuous modulated phase shift as the secret key to be used in higher-layer encryptions.
Practical Repetition-Aware Grammar Compression
Venue: arXiv cs.DS 2019
Year: 2019

The goal of grammar compression is to construct a small sized context free grammar which uniquely generates the input text data. Among grammar compression methods, RePair is known for its good practical compression performance. MR-RePair was recently proposed as an improvement to RePair for constructing small-sized context free grammar for repetitive text data. However, a compact encoding scheme has not been discussed for MR-RePair. We propose a practical encoding method for MR-RePair and show its effectiveness through comparative experiments. Moreover, we extend MR-RePair to run-length context free grammar and design a novel variant for it called RL-MR-RePair. We experimentally demonstrate that a compression scheme consisting of RL-MR-RePair and the proposed encoding method show good performance on real repetitive datasets.
A Generalization of Principal Component Analysis
Venue: arXiv cs.LG 2019
Year: 2019

Conventional principal component analysis (PCA) finds a principal vector that maximizes the sum of second powers of principal components. We consider a generalized PCA that aims at maximizing the sum of an arbitrary convex function of principal components. We present a gradient ascent algorithm to solve the problem. For the kernel version of generalized PCA, we show that the solutions can be obtained as fixed points of a simple single-layer recurrent neural network. We also evaluate our algorithms on different datasets.
Computer Assisted Access to Justice via Formal Jurisprudence Modeling
Venue: arXiv cs.CY 2019
Year: 2019

This paper discusses an internet-based system for enabling people to self-assess their legal rights in a given situation, and a development methodology for such systems. The assessment process is based on a formal model of the relevant jurisprudence, exposed to the user through an interview. The model consists of a multi-dimensional space whose dimensions represent orthogonal jurisprudence aspects, and a decision graph that guides the user through that space. Self-assessment systems can revolutionize the way legal aid organizations help their clients, as they allow these organizations to deliver personalized help at internet scales. The proposed approach is validated through an implementation of a model for workers' rights when their employment ends. This model, describing Israeli law and developed in cooperation with a worker rights NGO, was ratified by external experts as accurate enough to be useful in real cases.
Balancing Multi-level Interactions for Session-based Recommendation
Venue: arXiv cs.IR 2019
Year: 2019

Predicting user actions based on anonymous sessions is a challenge to general recommendation systems because the lack of user profiles heavily limits data-driven models. Recently, session-based recommendation methods have achieved remarkable results in dealing with this task. However, the upper bound of performance can still be boosted through the innovative exploration of limited data. In this paper, we propose a novel method, namely Intra-and Inter-session Interaction-aware Graph-enhanced Network, to take inter-session item-level interactions into account. Different from existing intra-session item-level interactions and session-level collaborative information, our introduced data represents complex item-level interactions between different sessions. For mining the new data without breaking the equilibrium of the model between different interactions, we construct an intra-session graph and an inter-session graph for the current session. The former focuses on item-level interactions within a single session and the latter models those between items among neighborhood sessions. Then different approaches are employed to encode the information of two graphs according to different structures, and the generated latent vectors are combined to balance the model across different scopes. Experiments on real-world datasets verify that our method outperforms other state-of-the-art methods.
An Adaptive Step Toward the Multiphase Conjecture
Venue: arXiv cs.DS 2019
Year: 2019

In 2010, Pǎtraşcu proposed the following three-phase dynamic problem, as a candidate for proving polynomial lower bounds on the operational time of dynamic data structures: I: Preprocess a collection of sets $\vec{S} = S_1, \ldots , S_k \subseteq [n]$, where $k=\operatorname{poly}(n)$. II: A set $T\subseteq [n]$ is revealed, and the data structure updates its memory. III: An index $i \in [k]$ is revealed, and the data structure must determine if $S_i\cap T=^? \emptyset$. Pǎtraşcu conjectured that any data structure for the Multiphase problem must make $n^ε$ cell-probes in either Phase II or III, and showed that this would imply similar unconditional lower bounds on many important dynamic data structure problems. Alas, there has been almost no progress on this conjecture in the past decade since its introduction. We show an $\tildeΩ(\sqrt{n})$ cell-probe lower bound on the Multiphase problem for data structures with general (adaptive) updates, and queries with unbounded but "layered" adaptivity. This result captures all known set-intersection data structures and significantly strengthens previous Multiphase lower bounds, which only captured non-adaptive data structures. Our main technical result is a communication lower bound on a 4-party variant of Pǎtraşcu's Number-On-Forehead Multiphase game, using information complexity techniques. We also show that a lower bound on Pǎtraşcu's original NOF game would imply a polynomial ($n^{1+ε}$) lower bound on the number of wires of any constant-depth circuit with arbitrary gates computing a random $\tilde{O}(n)\times n$ linear operator $x \mapsto Ax$, a long-standing open problem in circuit complexity. This suggests that the NOF conjecture is much stronger than its data structure counterpart.
Modelling and testing timed data-flow reactive systems in Coq from controlled natural-language requirements
Venue: arXiv cs.SE 2019
Year: 2019

Data-flow reactive systems (DFRSs) are a class of embedded systems whose inputs and outputs are always available as signals. Input signals can be seen as data provided by sensors, whereas the output data are provided to system actuators. In previous works, verifying properties of DFRS models was accomplished in a programmatic way, with no formal guarantees, and test cases were generated by translating theses models into other notations. Here, we use Coq as a single framework to specify and verify DFRS models. Moreover, the specification of DFRSs in Coq is automatically derived from controlled natural-language requirements. Property verification is defined in both logical and functional terms. The latter allows for easier proof construction. Tests are generated with the support of the QuickChick tool. Considering examples from the literature, but also from the aerospace industry (Embraer), our testing strategy was evaluated in terms of performance and the ability to detect defects generated by mutation; within 8 seconds, we achieved an average mutation score of 75.80%.
Use Case Evolution Analysis based on Graph Transformation with Negative Application Conditions
Venue: arXiv cs.SE 2019
Year: 2019

Use Case (UC) quality impacts the overall quality and defect rate of a system, as they specify the expected behavior of an implementation. In a previous work, we have defined an approach for a step-by-step translation from UCs written in natural language to a formal description in terms of Graph Transformation (GT), where each step of the UC was translated to a transformation rule. This UC formalisation enables the detection of several specification problems even before an actual implementation is produced, thus reducing development costs. In this paper, we extend our approach to handle UC evolution by defining \emph{evolution rules}, which are described as higher-order rules, simultaneously changing the behaviour of a set of transformation rules. We also support the use of \emph{negative application conditions (NAC)} associated both to the transformation and evolution rules. Analysis of the interplay between the evolution rules and the rules describing UC steps shows the effects of an evolution and serves to identify potential impacts, even before the changes are actually carried out. Besides defining the theoretical foundations of UC evolution with NACs, we have implemented the evolution analysis technique in the Verigraph tool and used it to verify impacts in 3 different case studies. The results demonstrate the applicability and usefulness of our approach to help developers in the evolution process based on UCs.
Asymptotic Divergences and Strong Dichotomy
Venue: arXiv cs.IT 2019
Year: 2019

The Schnorr-Stimm dichotomy theorem concerns finite-state gamblers that bet on infinite sequences of symbols taken from a finite alphabet $Σ$. In this paper we use the Kullback-Leibler divergence to formulate the $\textit{lower asymptotic divergence}$ $\text{div}(S||α)$ of a probability measure $α$ on $Σ$ from a sequence $S$ over $Σ$ and the $\textit{upper asymptotic divergence}$ $\text{Div}(S||α)$ of $α$ from $S$ in such a way that a sequence $S$ is $α$-normal (meaning that every string $w$ has asymptotic frequency $α(w)$ in $S$) if and only if $\text{Div}(S||α)=0$. We also use the Kullback-Leibler divergence to quantify the $\textit{total risk }$ $\text{Risk}_G(w)$ that a finite-state gambler $G$ takes when betting along a prefix $w$ of $S$. Our main theorem is a $\textit{strong dichotomy theorem}$ that uses the above notions to $\textit{quantify}$ the exponential rates of winning and losing on the two sides of the Schnorr-Stimm dichotomy theorem (with the latter routinely extended from normality to $α$-normality). Modulo asymptotic caveats in the paper, our strong dichotomy theorem says that the following two things hold for prefixes $w$ of $S$. (1) The infinitely-often exponential rate of winning is $2^{\text{Div}(S||α)|w|}$. (2) The exponential rate of loss is $2^{-\text{Risk}_G(w)}$. We also use (1) to show that $1-\text{Div}(S||α)/c$, where $c= \log(1/ \min_{a\inΣ}α(a))$, is an upper bound on the finite-state $α$-dimension of $S$ and prove the dual fact that $1-\text{div}(S||α)/c$ is an upper bound on the finite-state strong $α$-dimension of $S$.
Dual Illumination Estimation for Robust Exposure Correction
Venue: arXiv cs.CV 2019
Year: 2019

Exposure correction is one of the fundamental tasks in image processing and computational photography. While various methods have been proposed, they either fail to produce visually pleasing results, or only work well for limited types of image (e.g., underexposed images). In this paper, we present a novel automatic exposure correction method, which is able to robustly produce high-quality results for images of various exposure conditions (e.g., underexposed, overexposed, and partially under- and over-exposed). At the core of our approach is the proposed dual illumination estimation, where we separately cast the under- and over-exposure correction as trivial illumination estimation of the input image and the inverted input image. By performing dual illumination estimation, we obtain two intermediate exposure correction results for the input image, with one fixes the underexposed regions and the other one restores the overexposed regions. A multi-exposure image fusion technique is then employed to adaptively blend the visually best exposed parts in the two intermediate exposure correction images and the input image into a globally well-exposed image. Experiments on a number of challenging images demonstrate the effectiveness of the proposed approach and its superiority over the state-of-the-art methods and popular automatic exposure correction tools.
Human-Like Hybrid Caching in Software-defined Edge Cloud
Venue: arXiv cs.NI 2019
Year: 2019

With the development of Internet of Things (IoT) and communication technology, the number of next-generation IoT devices has increased explosively, and the delay requirement for content requests is becoming progressively higher. Fortunately, the edge-caching scheme can satisfy users' demands for low latency of content. However, the existing caching schemes are not smart enough. To address these challenges, we propose a human-like hybrid caching architecture based on the software defined edge cloud, which simultaneously considers the content popularity and the fine-grained user characteristics. Then, an optimization problem with a caching hit ratio as an optimization objective is formulated. To solve this problem, using reinforcement learning, we design a human-like hybrid caching algorithm. Extensive experiments show that compared with popular caching schemes, human-like hybrid caching schemes can improve the cache hit ratio by 20%.